Systems and methods for real-time target validation for image-guided radiation therapy

ABSTRACT

Systems and methods for real-time target validation during radiation treatment therapy based on real-time target displacement and radiation dosimetry measurements.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/565,900 filed Dec. 1, 2011, the content of which is herebyincorporated by reference in its entirety.

FIELD

The present disclosure relates generally to delivering radiation to atreatment target, and more particularly to systems, methods, andcomputer program products for real-time target validation duringimage-guided radiation therapy treatment.

BACKGROUND

Radiosurgery and radiotherapy systems are radiation therapy treatmentsystems that use external radiation beams to treat pathologicalanatomies (tumors, lesions, vascular malformations, nerve disorders,etc.) by delivering prescribed doses of radiation (X-rays, gamma rays,electrons, protons, and/or ions) to the pathological anatomy whileminimizing radiation exposure to the surrounding tissue and criticalanatomical structures. Radiotherapy is characterized by a low radiationdose per fraction (e.g., 100-200 centiGray), shorter fraction times(e.g., 10-30 minutes per treatment), and hyper fractionation (e.g.,30-45 fractions), and repeated treatments. Radiosurgery is characterizedby a relatively high radiation dose per fraction (e.g., 500-2000centiGray), extended treatment times per fraction (e.g., 30-60 minutesper treatment), and hypo-fractionation (e.g. 1-5 fractions or treatmentdays). Due to the high radiation dose delivered to the patient duringradiosurgery, radiosurgery requires high spatial accuracy to ensure thatthe tumor or abnormality (i.e., the target) receives the prescribed dosewhile the surrounding normal tissue is spared.

In general, radiosurgery treatments consist of several phases. First, aprecise three-dimensional (3D) map of the anatomical structures in thearea of interest (head, body, etc.) is constructed using any one of (orcombinations thereof) a computed tomography (CT), cone-beam CBCT,magnetic resonance imaging (MRI), positron emission tomography (PET), 3Drotational angiography (3DRA), or ultrasound techniques. This determinesthe exact coordinates of the target within the anatomical structure,namely, locates the tumor or abnormality within the body and defines itsexact shape and size. Second, a motion path for the radiation beam iscomputed to deliver a dose distribution that the surgeon findsacceptable, taking into account a variety of medical constraints. Duringthis phase, a team of specialists develop a treatment plan using specialcomputer software to optimally irradiate the tumor and minimize dose tothe surrounding normal tissue by designing beams of radiation toconverge on the target area from different angles and planes. Third, theradiation treatment plan is executed. During this phase, the radiationdose is delivered to the patient according to the prescribed treatmentplan.

To help deliver radiation therapy to the target according to theradiation treatment plan, image-guided radiation therapy (IGRT) can beused. IGRT is a radiation therapy process that uses cross-sectionalimages of a patient's internal anatomy taken during the radiationtherapy treatment session (i.e., in-treatment images) to provideinformation about the patient's position. IGRT is thus a process offrequent two or three-dimensional imaging during the course of theradiation treatment used to direct the therapeutic radiation utilizingthe imaging coordinates of the actual radiation treatment plan. Thisensures that the patient is localized in the radiation treatment systemin the same position as planned, and that the patient is properlyaligned during the treatment. The IGRT imaging process is different fromthe imaging process used to delineate targets and organs in the planningprocess of radiation therapy (i.e., different from the pre-treatmentimages obtained during the first phase). Although, the IGRT processinvolves conformal radiation treatment guided by specialized imagingtests taken during the first phase, it does rely on the imagingmodalities from the planning process as the reference coordinates forlocalizing the patient during treatment. Thus, associated with eachimage-guided radiation therapy system is an imaging system to providein-treatment images that are used to set-up the radiation deliveryprocedure.

In-treatment images can include one or more two or three-dimensionalimages (typically X-ray) acquired at one or more different points ofview during treatment. There are a variety of ways to acquire thein-treatment images. In certain approaches, distinct independent imagingsystems and/or imaging methods are used for acquiring pre-treatment andin-treatment images, respectively. For example, a 3D IGRT could includelocalization of a cone-beam computed tomography (CBCT) dataset with aplanning computed tomography (CT) dataset, and a 2D IGRT could includematching planar kilovoltage (kV) radiographs or megavoltage (MV) imageswith digital reconstructed radiographs (DRRs) obtained from the planningCT. Alternatively, another approach is to use portal imaging systems. Inportal imaging systems, a detector is placed opposite the therapeuticradiation source to image the patient for setup and in-treatment images.Another approach is X-ray tomosynthesis which is an in-treatment imagingmodality for use in conjunction with radiation treatment systems. X-raytomosynthesis is a process of acquiring a number of two-dimensionalX-ray projection images of a target volume using X-rays that areincident upon the target volume at respective number of differentangles, followed by the mathematical processing of the two-dimensionalX-ray projection images to yield a set of one or more tomosynthesisreconstruction images representative of one or more respective slices ofthe target volume.

In image-guided radiotherapies, there are many factors that cancontribute to differences between the prescribed radiation dosedistribution and the actual dose delivered (i.e., the actual dosedelivered to the target during the radiation treatment). One such factoris uncertainty in the patient's position in the radiation therapysystem. Other factors involve uncertainty that is introduced by changesthat can occur during the course of the patient's treatment. Suchchanges can include random errors, such as small differences in apatient's setup position. Other sources are attributable tophysiological changes that might occur if a patient's tumor regresses orif the patient loses weight during therapy. Another category ofuncertainty includes motion. Motion can potentially overlap with eitherof the categories as some motion might be more random and unpredictable,whereas other motion can be more regular. These uncertainties can affectthe quality of a patient's treatment and the actual radiation dosedelivered to the target.

In existing radiosurgery therapy systems, establishing precision oftherapeutic dose delivery is done by carefully calibrating the positionand orientation of a well characterized imaging system with respect tothe therapeutic radiation beam used during the treatment. The accuracyof the radiosurgery is, therefore, dependent on the fidelity with whichthis calibration process if performed.

In stereotactic radiosurgery (SRS), which is a highly precise form ofradiation therapy used to treat tumors and abnormalities of the brain,3D imaging, such as CT, MRI, and PET/CT, is used to generatepre-treatment images to locate the tumor or abnormality within the bodyand define its exact size and shape. SRS also relies on systems toimmobilize and carefully position the patient's head during the initialimaging phase (pre-treatment imaging) as well as during the radiationtherapy session. Therefore, in SRS, the pre-treatment images show theexact location of the tumor in relation to the head frame. Instereotactic radiosurgery, this calibration can utilize rigid externalframe-based head immobilization which, when properly calibrated,reliably locates the head or other body parts in a known position withrespect to the therapeutic beams. However, if the calibration is doneincorrectly or if post pre-treatment imaging the head slips inside theexternal frame or the radiosurgery instrument is knocked out ofalignment after calibration, a user may not know, and seriousconsequences are possible.

In the existing radiation therapies, therefore, radiation delivery ismade based on the assumption that the radiation treatment plan wasdeveloped based on correct information, the position of the radiationbeam relative to the patient set-up is correctly calibrated, and thatthe radiation therapy system not only functions properly but that italso functions based on correct and consistent external inputs used toprogram the system. However, if the calibration of the support device,for example, is incorrect, or the radiation therapy system functionsimproperly, or the treatment plan may include incorrect information, anincorrect dose will be delivered to the target during treatment even ifthe radiation therapy system operates as instructed.

Also, radiation therapy detectors typically are either not designed todetect high energy therapeutic beams or not designed to be constantlyoperating during radiosurgery, and therefore real-time validation of thetarget treatment volume is not feasible.

SUMMARY

The accuracy in delivering a predicted radiation dose to a target basedon a predetermined treatment plan plays an important role in theultimate success or failure of the radiation treatment. Inaccurate dosedelivery can result in either insufficient radiation for cure, orexcessive radiation to nearby healthy tissue. Therefore, validating inreal-time that the predicted radiation dose is delivered to the actualtreatment target volume according to the treatment plan is desirable.

The present disclosure includes systems, methods, and computer programproducts for confirming in real-time that a therapeutic beam in aradiation therapy system is delivered to the intended target.

The present disclosure also provides systems, methods, and computerprogram products for validating the target in real-time duringimage-guided radiosurgery.

The present disclosure also provides systems, methods, and computerprogram products for real-time target validation during radiationtreatment therapy based on real-time target displacement and radiationdosimetry measurements.

The present disclosure also provides systems, methods, and computerprogram products for corroborating in real-time the spatial position andorientation of the actual treatment target volume with respect to thepretreatment planning target volume.

The present disclosure also provides systems, methods, and computerprogram products that serve as quality assurance tools for the accuracyof radiosurgery in real-time, and to allow surgeons to interrupt thesurgery to recalibrate the system during the treatment.

In various embodiments, the systems, methods and computer programproducts include means for real-time target validation based on adetermination of a three-dimensional (3D) path of the radiationpenetrating the target and the amount of delivered therapeutic radiationdose.

In various embodiments, the real-time validation process includesdetermining the three-dimensional (3D) path of the radiation penetratingthe target based on transmitted radiation intensity measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.The invention will be best understood by reading the ensuingspecification in conjunction with the drawing figures, in which likeelements are designated by like reference numerals. As used herein,various embodiments can mean some or all embodiments.

FIGS. 1A and 1B are perspective views of a radiation therapy systemaccording to an embodiment of the invention;

FIG. 2 is a schematic illustration of the radiation therapy system ofFIG. 1;

FIG. 3 is a schematic illustration of generating DRRs according tovarious embodiments;

FIG. 4A illustrates a library of (keV) synthesized DRRs according tovarious embodiments;

FIG. 4B illustrates a library of (MeV) synthesized DRRs according tovarious embodiments;

FIGS. 5A-5B illustrate a library of DRRs for different gantry angles;

FIG. 6 illustrates a correlation graph between DRRs and live images atdifferent gantry angles;

FIGS. 7A-7B illustrate correlation between detector resolution anddisplacement detection;

FIGS. 8A-8C show differences between different image reconstructionmethods;

FIG. 9 is a flow chart of a real-time target validation method accordingto various embodiments;

FIG. 10 is a flow chart of a method to determine target displacementaccording to various embodiments;

FIG. 11 is a flow chart of a method to reconstruct an actual 3D targetvolume according to various embodiments;

FIG. 12 is a schematic diagram of computer-based modules comprising asequence of programmed instructions used in the radiation therapytreatment system of FIG. 1 to implement a real-time target validationprocess;

FIG. 13 illustrates a multi-layered media (MCML) simulation method;

FIG. 14A illustrates a design matrix Φ for a collection of linearprojections; and

FIG. 14B illustrates a design matrix Φ for a collection of linearprojections for volume reconstruction.

DETAILED DESCRIPTION

Real-time validation according to various embodiments can includereal-time displacement detection, real-time target volumereconstruction, real-time feedback, and real-time dosimeter monitoring.The displacement detection, the target volume reconstruction, and thedosimeter monitoring functions are based on computational algorithmsincorporating transmitted therapeutic radiation trajectory and radiationfluence (i.e., energy per unit area) measurements.

Displacement detection is performed to ascertain any displacement of atherapeutic radiation beam with respect to the target. This detectioncan be obtained, for example, by correlating a previously generated andstored library of projection images of predicted beam trajectories withlive projection images of beam trajectories obtained during radiationtreatment. The live and the stored projection images can includeradiation fluence information. During displacement detection, acomparison of the predicted radiation fluence from the library ofpredicted beam trajectories and the actual measured radiation fluencecan be made to compute similarity measures between the two sets offluences.

Real-time target volume reconstruction is performed to reconstruct a 3Dtarget volume which is actually targeted by the therapeutic radiationbeam. Such information can in real-time confirm to the operator that thetreatment is being executed as planned. This can be done usingreconstruction algorithms which use information based on the obtainedprojection images, the transmitted radiation fluence, and the geometryof the targeting device, and as explanatory variables the positions andorientations of the therapeutic beam. Parallel computing architecturesmay be used to accelerate the computation.

Real-time dosimetry is performed to compare in real-time the actual dosedelivered to the target during the radiation treatment and the plannedradiation dose. This comparison provides added quality assurance thatthe treatment therapy is being properly administered.

Real-time feedback is performed to correct the position of thetherapeutic beam in real-time should any displacement of the target bedetermined or recorded. The feedback is triggered by a detecteddisplacement signal being above a threshold displacement signal. Thedetected displacement signal is obtained by comparing the library ofpredicted projection images with the in-treatment image X-ray fluenceinformation, and/or by comparing the the in-treatment dosimetrymeasurement with previously recorded dosimetry measurements. If thedetected displacement signal is larger than a predetermined thresholddisplacement signal, or if the detected dose is significantly largerthan a prescribed dose, the feedback signal can automatically cause theradiation treatment to stop or can indicate to a medical personnel(e.g., surgeon) that the radiation delivery is not as planned and allowthe medical personnel to stop the radiation treatment. This gives theoperating surgeon the option to stop the treatment or surgery and torecalibrate the radiation treatment device or the treatment procedureaccordingly.

FIGS. 1A and 1B illustrate an exemplary radiation therapy treatmentsystem 100 that can provide radiation therapy to a patient 5 and providereal-time target validation. The radiation therapy treatment can includephoton-based radiation therapy, particle therapy, electron beam therapy,or any other type of treatment therapy. In an embodiment, the radiationtherapy treatment system 100 includes a radiation treatment device 10,such as, but not limited to, a radiosurgery device, which can include agantry 7 supporting a radiation module 8 which includes one or moreradiation sources 3 and a linear accelerator 2 operable to generate abeam of kV or MV X-ray radiation. The gantry 7 can be a ring gantry(i.e., it extends through a full 360 degree arc to create a completering or circle), but other types of mounting arrangements may also beemployed. For example, a C-type, partial ring gantry, or robotic armcould be used. Any other framework capable of positioning the radiationmodule 8 at various rotational and/or axial positions relative to thepatient 5 may also be used. The radiation source 3 can also travel in anon-circular path that does not follow the shape of the gantry 7. Theradiation module 8 can also include a modulation device (not shown)operable to modulate the radiation beam as well as to direct atherapeutic radiation beam toward the patient 5 and toward a portion ofthe patient which is desired to be irradiated. The portion desired to beirradiated is referred to as the target or target region or a region ofinterest. The patient 5 may have one or more regions of interest thatneed to be irradiated. A collimation device (not shown) may be includedin the modulation device to define and adjust the size of an aperturethrough which the radiation beam may pass from the source 3 toward thepatient 5. The collimation device may be controlled by an actuator (notshown) which can be controlled by a computer processing system 50 and/ora controller 40.

In an embodiment, the radiation therapy includes, but is not limited to,a kV or MV energy image-guided arc-type radiation therapy orradiosurgery, such as, but not limited to, stereotactic radiosurgery(SRS). The radiosurgery device 10 can be a self-shielded radiosurgerydevice, such as but not limited to, an arc-type X-ray image guidedself-shielded radiosurgery device. Such a set-up enables the radiationsensor(s) 4 to be positioned directly opposite the therapeutic radiationsource(s) 3 because it can be positioned outside specialized radiationbunkers. This setup makes it possible to continuously detect thetherapeutic beam energy after it passes through the target volume.

To provide image-guidance, the radiosurgery device 10 includes animaging device 30 for detecting and recording the transmission of thetherapeutic X-ray beams from the source 3. The imaging device 30 can bea digital imaging device including a CMOS detector or an amorphoussilicon detector or any other radiation detectors capable of receivingthe therapeutic radiation that passes through the patient 5 and capableof measuring transmitted X-rays. These imaging devices can include, butare not limited to, segmented ion chambers, multiwire proportionalcounters, scintillating fibers, and Cerenkov detectors. The digitalimaging device 30 can be positioned so that the detector 4 and thetherapeutic radiation source 3 are arranged to be directly opposite fromeach other, and so that the detector 4 can continuously receive duringthe treatment the therapeutic radiation beams that passed through thetarget region of the patient 5. The imaging device 30 generally consistsof picture elements (pixels) that register the amount of radiation thatfalls on them and that convert the received amount of radiation into acorresponding number of electrons. The electrons are converted intoelectrical signals which are further processed using either the imagingdevice 30 or the computer 50. Such a configuration (i.e., digitalimaging detector(s) positioned opposite the therapeutic source(s))provides the ability to capture in real-time the energy and intensity ofthe therapeutic radiation transmitted through the target volume and togenerate two-dimensional (2D) in-treatment images of digitized X-raymeasurements.

In an embodiment, a photodetector system with a scintillating fiberdetector in connection with either a CCD camera, an a-Si TFT photodiodearray, or a CMOS sensor, or any other device capable of producing areproducible image with the level of light produced by the scintillatingfiber, is used. The scintillating optical fibers function as smallradiation detectors. These scintillating fibers can be distributed asdesired within a three-dimensional detection space so as to gatherradiation beam data, e.g., energy distribution within the space. Thephotodetector system includes an imager that converts the optical energytransmitted by the collection of optical fibers to an electric signalthat may be thereafter converted to a radiation dose. The photo-detectorimager can also take a 2D image of the input optical signals from thescintillating fibers.

Since each discrete therapeutic radiation beam that passes through thepatient 5 carries with it a unique signature of the specific anatomythrough which it passes, the detector 4 is operable to measure theintensity of the transmitted therapeutic radiation through the specificanatomy, the amount of radiation absorbed by the specific anatomy, andthe trajectory of the beam through the anatomy. A precise analysis ofthe transmitted beams (beam trajectory) through the patient, togetherwith a detector pixel by pixel measurement of the beam fluence (i.e.,energy per unit area) allows for a determination of thethree-dimensional (3D) path of therapeutic radiation penetrating a knownobject, when the radiation scatter of the anatomy in question is known.The radiation scatter of the anatomy in question can be determined by CTscanning, for example, prior to commencing the radiation therapy. Theradiation scatter can be estimated based on knowledge of the CT number(Hounsfield unit HU) derived from previous or current scans. Knowing the3D path of radiation that is delivered to the target volume duringtreatment allows for real-time determination of the spatial position andorientation of the actual treatment target volume, i.e., the actualvolume of the target that is being irradiated during the treatment.Corroborating the spatial position and orientation of the actualtreatment target volume in real-time with respect to the pretreatmentplanning target volume, i.e., the volume of the target planned duringthe planning phase, can validate the prescribed radiation treatmentdelivery. In addition, knowledge of the radiation dose delivered to theactual target volume in real-time can corroborate the radiation dosedelivery treatment plan.

Generally, in radiosurgery, given the small volume of the targets(approximately 3 cm in diameter) and the greater penetration and lessscatter of the megavolt (MV) therapeutic radiation, the resultingin-treatment images may have a limited field, poor resolution, and maybe noisy, and therefore precise analysis of the transmitted beams can bedifficult because at MV energies Compton Scattering is the dominantprocess until the energy of the photon beam is more than 10 MeV,whereas, at lower (keV) energies there is a competing mechanism, such asthe photoelectric effect, to Compton Scattering. However, by usingstatistical measures and fast computational algorithms as describedherein, embodiments can determine in real-time the 3D path oftherapeutic radiation penetrating the treatment target volume, as wellas the actual therapeutic dose delivered to the treatment target volume.The actual 3D path of therapeutic radiation penetrating the treatmenttarget can also be corroborated with the prescribed 3D path, andtherefore, real-time validation of the radiation delivery treatment plancan be achieved.

In operation, prior to radiation therapy treatment, a previouslyconstructed model which embodies the 3D geometry of the patient and thelinear accelerator and any additional hardware, such as, but not limitedto, collimators, which are used as the final therapeutic radiation fielddefining devices, of the specific radiation treatment device 10 togetherwith the patient's 5 anatomy is stored in a storage device in thecomputer 50. In order to generate a treatment plan, the patient 5undergoing radiosurgery or radiotherapy treatment is imaged withdiagnostic CT scanning, for example. Other forms of diagnostic scanning,such as, but not limited to, MRI can also be used. The CT scan providespre-treatment images including a 3D Hounsfield map of the anatomy inquestion. Since most forms of radiosurgery require placement of alight-weight head frame (when the anatomy in question is the head) tohelp define the exact location of the tumor and to help immobilize thehead so there is no movement during the treatment, the CT scan is donewith the head frame on. A treatment planning module (shown in FIG. 12)can configure the computer 50 using specially designed planning softwareto precisely determine the treatment plan for the patient 5.Alternatively, the pre-treatment images and the treatment plan aredeveloped externally and the information is transferred to the computer50. Once the treatment plan is generated, the patient 5 is positioned ona sliding surgical bed 1 encircled by the linear accelerator 2. Thissubsequent position (bounded by certain approximations) of theconstrained patient's anatomy (for example, the head with the headframe) within the radiosurgery device 10 can be estimated prior toradiosurgery.

The estimated position of the patient 5 anatomy within the radiosurgerydevice 10, combined with the previously stored 3D model of the patientalong with the linear accelerator geometry and any additional hardwareused in radiotherapy to define the therapeutic radiation field of theradiation treatment device 10, enable algorithms included in theanalysis module (shown in FIG. 12) to predict the approximate trajectoryof one or more treatment beams through the anatomy in question (or eachof the treatment beam planned in the treatment plan). In order togenerate the prediction, a library of digitally reconstructedradiographs (DRRs) is synthesized by simulating the projection oftherapeutic beams at the targeted volume at different isocenters ofradiation and different projection angles, as shown in FIG. 3, forexample. One of the more rigorous methods by which projection images forindividual beams can be synthesized is the Monte Carlo simulation formulti-layered media (MCML) method. The MCML method models how steadylight transports in multi-layered media. It assumes infinitely widelayers and models an incident pencil beam that is perpendicular to thesurface. The MCML approach scores absorption, reflectance, andtransmittance of light in the tissue. The key steps in the MCML programare shown in the diagram of FIG. 13.

Each photon packet undergoes three steps, hop, drop, and spin that arerepeated continuously in the simulation. The hop step moves the photonpackets from a current position to the next interaction site bycomputing the step size through sampling a probability distributionbased on the photon's free path. The drop step adjusts the photonpacket's weight to simulate absorption based on the absorptioncoefficient at the site of the interaction. The spin step computes thescattering angle. When a photon exits the tissue through the top orbottom layer, it is terminated. If the photon weight has reached athreshold value, a survival roulette is performed to determine iftracking of the photon packet should end. If the photon survives, itsweight is increased. Using this model, projection images for individualbeams can be synthesized.

Taking advantage of the fact that the therapeutic beam has megavoltage(MeV) energy, which leads to little scattering, the MCML simulation canbe simplified via ray tracking models which simulate primary radiationtransmission only and do not consider any scattered radiation eitherfrom the patient's body or the collimator. In such models, the primarytransmission I can be calculated by: I=I_(o)R⁻²exp(−∫μdx), where I_(o)is the incident fluence without attenuation, μ is the total linearattenuation coefficient, and R is the distance from the virtual sourceposition to the point of calculation. This approximation method can beused to speed up the synthesizing process.

Parallel computing architectures, such as, but not limited to, GPU(graphic processing unit) and multi-core environment, or distributedcomputing with GPU clusters installed in different computers within alocal area network (LAN), or parallel computing architecture with acluster of servers within high-speed interconnect, can be employed toaccelerate the simulation computation. The parallelization of the MCMLmodel on parallel computing architectures can be done by employing anembarrassingly parallel computation where the MCML algorithm is keptintact and paralleled across an expanded search space of head positionsand beam trajectories, or through divide and conquer parallelizationwhich uses optimization inside the MCML model by minimizing divergentbehavior due to photon-electron coupling, for example. Thesecalculations can be performed over an expanded search space of headpositions and beam trajectories, and the resulting images can be storedas synthetic DRR images in a look up table in the computer 50. FIG. 4illustrates a library of DRRs obtained under different energy levelsusing the ray tracing method. FIG. 4A shows synthesized (keV) DRRs, andFIG. 4B shows synthesized (MeV) DRRs.

Also prior to the commencement of the radiation treatment, and prior tothe positioning of the patient 5 on the surgical bed 1, a trial run or across check of the dose rates and positions of the device 10 for theprescribed radiation dose can be performed. The radiation doseprediction module uses this information to determine a baseline forradiation dosimetry during the treatment. Since the therapeutic beamassumes a specific shape based on the shape and size of the collimatorpresent in the radiation therapy device 10, this shape information alsoprovides a signature for the prescribed therapeutic treatment for eachprojection angle. The shape and dose distribution for each projectionangle obtained during the cross check is stored in the computer 50 as alibrary of shape and dose distributions. This information can be usedfor real-time dosimetry monitoring during the treatment.

FIG. 2 illustrates schematically the radiation treatment device 10 usedfor real-time validation. In operation, during the treatment session,namely, during delivery of the radiation therapy treatment according tothe treatment plan, the detector(s) 4 in the digital imaging device 30receives data from different projection angles θθ≤360° as the linearaccelerator 2 rotates around the gantry 7 and emits therapeuticradiation toward the patient 5. The digital imaging device 30 collectsin real time the transmitted energy and produces 2D digital images whichrepresent two-dimensional arrays of digitized X-ray measurements. Theselive in-treatment images are sent to the computer 50 for furtherprocessing. In another embodiment, both, the live in-treatment images,as well as images obtained using an additional kV source (not shown)obtained are sent to the computer 50 for further processing.

In an embodiment, digital tomosynthesis is performed to generate thein-treatment images. To acquire the images, the therapeutic X-ray source3 emits X-rays toward the target from a discrete number of angles duringone sweep of the linear accelerator and produces a plurality ofcross-sectional images which are reconstructed to produce images atdifferent planes within the patient 5. The transmitted X-ray radiationis detected using a flat panel digital detector 4. Shift- and add (SAA)reconstruction algorithms and/or other applicable mathematicalde-blurring techniques can be used to reconstructs the in-treatmentimages and to improve quality of the images.

Embodiments of the system 100 can include the computer 50 includingtypical hardware such as a processor, and an operating system forrunning various software programs and/or communication applications. Thecomputer can include software programs that operate to communicate withthe radiation therapy device 10, and the software programs are alsooperable to receive data from any external software programs andhardware. The computer 50 can also include any suitable input/outputdevices adapted to be accessed by medical personnel, as well as I/Ointerfaces, storage devices, memory, keyboard, mouse, monitor, printers,scanner, etc. The computer 50 can also be networked with other computersand radiation therapy systems 20. Both the radiation therapy device 10and the computer 50 can communicate with a network as well as a databaseand servers. The computer 50 is also adapted to transfer medical imagerelated data between different pieces of medical equipment.

The system 100 can also include a plurality of modules containingprogrammed instructions which, as shown in FIG. 12, communicate witheach other and cause the system 100 to perform different functionsrelated to radiation therapy/surgery, as discussed herein, whenexecuted. For example, the system 100 can include a treatment planmodule operable to generate the treatment plan for the patient 5 basedon a plurality of data input to the system by the medical personnel, apatient positioning module operable to position and align the patient 5with respect to the isocenter of the gantry 7 for a particular radiationtherapy treatment, an image acquiring module operable to instruct theradiation therapy device (e.g., radiosurgery device) 10 to acquireimages of the patient 5 prior to the radiation therapy treatment (i.e.,pre-treatment images used for treatment planning and patientpositioning) and/or during the radiation therapy treatment (i.e.,in-treatment images), and to instruct other imaging devices or systems20 to acquire images of the patient 5.

The system 100 can further include a radiation dose prediction moduleoperable to predict a dose to be delivered to the patient 5 beforecommencement of the radiation treatment therapy, a dose calculationmodule operable to calculate the actual dose delivered to the patient 5during radiation therapy treatment, a treatment delivery module operableto instruct the radiation therapy device 10 to deliver the treatmentplan to the patient 5, a correlation module operable to correlate theDRRs with the in-treatment images obtained during radiation therapy, acomputation module operable to reconstruct a three-dimensional targetvolume from in-treatment images, an analysis module operable to computedisplacement measurements, and a feedback module operable to instructthe controller in real-time to stop radiation therapy based on acomparison of the calculated displacement with a predetermined thresholdvalue (range). The modules can be written in the C or C++ programminglanguage, for example.

Displacement Measurement

Upon receipt of the live in-treatment images, the correlation module inthe computer 50 executes one or more algorithms to compare the livein-treatment images with the stored library of synthetic DRRs, and thecorrelation module executes one or more algorithms to compute similaritymeasures between the live images and the DRRs. Since the informationcontained in the live images are primarily due to bone structures (forinstance the skull if the surgery is performed for treating a braintumor) over a span of projection angles, the series of collected liveimages carry variations that constitute unique signatures of the skullthickness and curvatures. For example, the variation of collected liveimages over a span of projection angles will be different when suchprojections are focused over the thickest or thinnest part of the skull.Therefore, one parameter (metric) that can be estimated is thecorrelation between the live in-treatment images (i.e., live imageseries) and the synthesized DRRs over a span of projection angles.Variations in the anatomy, such as skull thickness, imaging geometry,tissue density, tissue type, tissue attenuation coefficients, tumorproperties, etc., can also be utilized to generate similarity measuresbetween synthesized DRRs and in-treatment images. These variations areeffective indicators for detecting displacement. This type of similaritymeasurement is in the space of therapeutic fluence.

The correlation between these two sets of images, namely, the livein-treatment images and the synthesized DRRs, results in a statisticalmeasure of correspondence. Based on the measured correspondence, aseries of beam trajectories, and locations of the targeted energybetween the two sets of images can be corroborated. A change in thecorrelation between the beam trajectories and locations of the targetedenergy and the beam trajectories and energy locations in the DRRsindicates that the target is displaced. If the target displacement islarger than a certain predetermined threshold value, a stop signal isgenerated by the computation module and sent as a feedback signal to thecontroller 40, which upon receiving the feedback signal, can generate anoutput prompting the surgeon to optionally interrupt the radiosurgery,or the feedback signal can be used to automatically interrupt theradiosurgery. This process allows a surgeon to confirm the fidelity ofradiosurgery during the procedure.

A library of synthesized DRRs obtained for different gantry angles(i.e., projection angles) is shown in FIGS. 5A-5B. FIG. 5A shows alibrary of synthesized DRRs for gantry angles spanning a range between0-30° with a 1 degree increment (i.e., one DRR image for every anglebetween 0° and 30°), and FIG. 5B shows DRRs for projections at 0°, 45°,90°, 135°, 180σ, and 270°. The correlation over a series of projectionsangles between live in-treatment images obtained using a process asshown and described throughout the specification and the synthesizedDRRs is shown in FIG. 6. A significant change in the correlation betweenthe beam trajectories and locations of the targeted energy and the beamtrajectories and energy locations in the DRRs can be seen in the graph.This is an indication that the target is displaced.

A study of the impact of detector resolution on displacement is shown inFIGS. 7A-7B. FIG. 7A illustrates three different images obtained usingdetectors having different resolutions, with the first detector having aresolution of 200 pixel/mm, the second detector a resolution of 400pixel/mm, and a third detector a resolution of 800 pixel/mm. FIG. 7Billustrates that resolution has no significant impact on thedisplacement, and therefore, a detector with high resolution is notrequired for the purpose of displacement detection.

As an alternative to the therapeutic fluence space correlation metricdescribed above, a compressive sensing algorithm can also be applied.While the correlation approach described above formulates thetherapeutic beam as a forward problem, compressive sensing algorithm cancast it as an inverse problem. In the inverse problem, one can solve foran estimated thickness of the skull, for example, and then compute acorrelation between the estimated and prescribed thicknesses. Thereby,the similarity measure will be cast back in a space of skull thickness(when the target is the brain tumor for instance), instead of thefluence space, using a compressive sensing algorithm.

The compressive sensing algorithm is designed to detect signal changesusing incomplete and noisy measurements. This is beneficial because inorder to validate a target in real time during radiosurgery, forexample, one cannot afford to wait until the completion of the surgeryto get enough samples (projection angles) for computation because bythen it would be too late to correct the displacement and error. Sincethe objective is to validate the target in real-time, constructingmeaningful quantities online using as fewer number of samples aspossible is key. Therefore, an undersampled environment is present.

The essence of compressive sensing is to solve, in a linear system, forunknown variables using under-sampled measurements. Using thistechnique, in-treatment images using the therapeutic beam can beacquired over a span of projection angles as under-sampled measurementsy, the transmittance over the therapeutic target can be modeled asdesign matrix Φ, and solutions x for signatures of the skull (such asbut not limited to, skull thickness and curvature) can be acquired.Specifically, since the under-sampled measurements are sparse in thespace of projection angles, sparsity can be modeled using a convex formof a sparse promoting norm, such as an L1 norm, and the linear systemcan be solved for the convex optimization problem. Since the imagesignatures are piece-wise constant, total variation (TV) can be employedas a type of structured sparsity to enhance the quality of thealgorithm. In addition, theoretical guarantees for the minimum number ofprojection angles to detect signal changes can be predicted. This formof compressive sensing tackles the problem of solving an underdeterminedlinear system, with fewer samples compared to unknowns.

Suppose the unknown variable is x, a collection of linear projections isrepresented by design matrix Φ, and measurement is represented by y, asillustrated in FIG. 14A. In the case of radiosurgery, the unknownvariable x could describe the properties of the skull, such as, but notlimited to, thickness and position. The design matrix Φ could denote thepath of therapeutic beam from different projection angles. Measurement ycould be the energy X-ray fluence acquired by the detector 4.

Compressive sensing exploits the fact that the measurement is limited inthe space of projections, and tackles the problem by explicitly imposingsparsity promoting regularization in the reconstruction algorithm. Thecompressive sensing solution can be formulated as the followingconstrained optimization:

$\begin{matrix}{\hat{x} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {H(x)}}} + {J(x)}}} & (1)\end{matrix}$

where the first term is the fidelity term and the second term is theregularization term. The fidelity term is modeled as the square errorbetween reconstructed signal and measurement H(x)=∥y−Φx∥₂ ². Theregularization term takes the form of a sparsity promoting norm.Depending on the property of the anatomy in question, different types ofsparsity promoting norms can be used in the optimization. When theconvex form of sparsity promoting norm is chosen, both algorithms A andB described below leverage convex optimization techniques.

Model A

The simplest form of sparsity can be modeled as an L1 normJ(x)=λ∥x∥_(l), derived from a Laplacian prior. With such regularization,a Model A algorithm can be obtained, which takes the form:

$\begin{matrix}{\hat{x} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {{y - {\Phi \; x}}}_{2}^{2}}} + {\lambda {x}_{1}}}} & (2)\end{matrix}$

Since there is no closed-form solution for Model A due to thenon-differentiability of the L1 norm, an iterative algorithm, such as,but not limited to, the linearized Bregman algorithm can be used tosolve the problem for this model.

Algorithm for Model A

Linearized Bregman Algorithm:

Define Bregman divergence as D_(J) ^(P)(u,v)=J(u)−J(v)−

p,u−v

, where p denotes the subgradient of J.

Seek a sequence of solution {u^(k)} in the iterative algorithm, whereeach subproblem is the solution of the following minimization:

$\begin{matrix}{x^{k + 1} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {D_{J}^{p^{k}}\left( {x,x^{k}} \right)}}} + {H(x)}}} & (3)\end{matrix}$

In order to solve for this problem more efficiently, a linearizationtechnique can be applied to the fidelity term,

$\begin{matrix}{x^{k + 1} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {D_{J}^{p^{k}}\left( {x,x^{k}} \right)}}} + {\langle{{\nabla{H\left( x^{k} \right)}},x}\rangle} + {\frac{1}{2\alpha}{{x - x^{k}}}^{2}}}} & (4)\end{matrix}$

and an update for subgradient p is specified based on an optimalitycondition of the minimization to obtain the linearized Bregman algorithmshown below.

Linearized Bregman Algorithm Input: Φ, y, J(x), H(x), λ>0, α>0Algorithm:

Initialize: k=0, x⁰=0, p⁰=0while stopping criteria not satisfied do

$\begin{matrix}{x^{k + 1} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {D_{J}^{p^{k}}\left( {x,x^{k}} \right)}}} + {\langle{{\nabla{H\left( x^{k} \right)}},x}\rangle} + {\frac{1}{2\alpha}{{x - x^{k}}}^{2}}}} & (5) \\{p^{k + 1} = {p^{k} - {\nabla{H\left( x^{k} \right)}} - {\frac{1}{\alpha}\left( {x^{k + 1} - x^{k}} \right)}}} & (6)\end{matrix}$

If x^(k+1)=x^(k), then apply kicking

Update k=k+1  (7)

end whileOutput: optimal solution x^(k)In this algorithm, λ>0 is a regularization parameter that controls thedegree of sparsity, α>0 is a constant specified by the user, whichaffects the speed of the algorithm, k=1, . . . , n is an integer thatindexes the iterate of the algorithm, and p^(k) is the subgradient onJ(x). The regularization parameter can be determined based on priorknowledge or by cross validation. The linearized Bregman algorithmcomputes the full regularization path (solution path) corresponding todifferent levels of sparsity in a computationally efficient manner. Thelinearized Bregman algorithm has competitive scaling with datadimension, thereby making it an ideal algorithm for large-scale datacomputation.

Model B

Depending on the structure of the anatomy in question, structuredsparsity can also be employed in the regularization term to improvereconstruction. One type of structured sparsity that can be used istotal variation (TV), which denotes a semi-norm with bounded variation:

TV(x)=∫|∇x|  (8)

Total variation characterizes an edge of the signal. In the case of bonestructures in the skull, since the anatomy is piecewise constant, totalvariation is suitable since sharp edges are preserved in thereconstruction. Therefore, by using J(x)=λ∫|∇x| and H(x)=∥y−Φx∥₂ ², thetotal variation regularized reconstruction leads to Model B:

$\begin{matrix}{\hat{x} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {\lambda {\int{{\nabla x}}}}}} + {{y - {\Phi \; x}}}_{2}^{2}}} & (9)\end{matrix}$

Model B differs from Model A in that it imposes sparsity in the space ofedge variation, where (TV) serves as a global operator for preservingedges. Like Model A, there is no closed-form solution for Model B andtherefore, an iterative algorithm is used. In order to solve for such amodel, the split Bregman algorithm can be used.

Algorithm for Model B Split Bregman Algorithm:

The split Bregman algorithm splits the energy between the first term Jand second term H via variable splitting technique, by introducinganother variable u=g(x). The optimization therefore becomes:

$\begin{matrix}{{\arg \mspace{14mu} {\min\limits_{x,u}\mspace{14mu} {J(u)}}} + {H(x)} + {\frac{\mu}{2}{{u - {g(x)}}}_{2}^{2}}} & (10)\end{matrix}$

Solving for a sequence of minimizers is next, where each subproblemsolves for:

$\begin{matrix}{{\arg \mspace{14mu} {\min\limits_{x,u}\mspace{14mu} {D_{J}^{p}\left( {u,u^{k}} \right)}}} + {D_{H}^{p}\left( {x,x^{k}} \right)} + {\frac{\alpha}{2}{{u - {g(x)}}}_{2}^{2}}} & (11)\end{matrix}$

The terms J and H are replaced by the associated Bregman divergencerespectively.

Split Bregman Algorithm Input: Φ, y, J(x), H(x), λ>0, α>0 Algorithm:

Initialize: k=0, x⁰=0, u⁰=0, p_(x) ⁰=0, p_(u) ⁰=0while stopping criteria not satisfied do

$\begin{matrix}{\left( {x^{k + 1},u^{k + 1}} \right) = {{\arg \mspace{14mu} {\min\limits_{x,u}\mspace{14mu} {J(u)}}} + {H(x)} - {\langle{p_{x}^{k},{x - x^{k}}}\rangle} - {\langle{p_{u}^{k},{u - u^{k}}}\rangle} + {\frac{\alpha}{2}{{u - {g(x)}}}_{2}^{2}}}} & (12) \\{\mspace{76mu} {p_{x}^{k + 1} = {p_{x}^{k} - {\mu {\nabla{g\left( x^{k + 1} \right)}^{T}}\left( {{g\left( x^{k + 1} \right)} - u^{k + 1}} \right)}}}} & (13) \\{\mspace{76mu} {p_{u}^{k + 1} = {p_{u}^{k} - {\alpha \left( {u^{k + 1} - {g\left( x^{k + 1} \right)}} \right)}}}} & (14) \\{\mspace{76mu} {{{Update}\mspace{14mu} k} = {k + 1}}} & (15)\end{matrix}$

end whileOutput: optimal solution x^(k)In this algorithm, λ>0 is a regularization parameter that controls thedegree of sparsity, α>0 is a constant specified by the user, whichaffects the speed of the algorithm, k=1, . . . , n is an integer thatindexes the iterate of the algorithm, p_(x) ^(k) is the subgradient onH(x), and p_(u) ^(k) is the subgradient on J(u). The regularizationparameter λ can be determined based on prior knowledge or by crossvalidation. The split Bregman algorithm is synonymous with theAlternating Direction Method of Multipliers (ADMM) method, which is alsoreferred to as the Alternating Direction Augmented Lagrangian (ADAL)method.

For displacement detection, either Model A or Model B can be used tosolve for the inverse problem. In either model, the input for thealgorithm can be: 1) design matrix Φ which is constructed by thecollection of projection angles and thickness of skull in eachprojection angles, and 2) measurement y, which is the detectedtherapeutic fluence. The output of the algorithm can be the estimatedthickness of the skull during the treatment, if the anatomy in questionis the brain. The choice between Model A and Model B depends on theimage quality acquired by the imaging device 30.

Since sensitivity of the therapeutic X-ray detection depends on thevariations of the anatomical structures, such as bone and fiduciallandmarks, the sensitivity can be further enhanced using contrastagents.

Volume Reconstruction

In addition to displacement detection, a 3D volume that is actuallytargeted by the therapeutic beam can also be reconstructed usingreconstruction algorithms included in the computation module (shown inFIG. 11) to be added as a second real-time validation parameter(metric). This additional information can in real-time confirm to anoperator that the treatment is being executed as planned. Computationalefficiency is the key to real-time 3D target volume reconstruction.Given the limited number of projections for the therapeutic beam andnoisy nature of the data, compressive sensing techniques can also beutilized to perform 3D volume reconstruction. For example, suppose theunknown variable is x, a collection of linear projections is representedby design matrix Φ, and measurement is represented by y, as illustratedin FIG. 14B. In the case of radiosurgery, the unknown variable x couldbe the positions of the target. The design matrix Φ could denote thecollection of X-ray projections. Measurement y could be the detectedtherapeutic energy. Specifically, for volume reconstruction, thegeometry of the therapeutic beam projections can be formulated as thedesign matrix Φ, while the transmitted X-ray fluence as observations y.The explanatory variables x that are sought are the positions of thetarget.

Compressive sensing exploits the fact that the measurement is limited inthe space of projections, and tackles the problem by explicitly imposingsparsity promoting regularization in the reconstruction algorithm. Thecompressive sensing solution can be formulated as the followingconstrained optimization:

$\begin{matrix}{\hat{x} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {H(x)}}} + {J(x)}}} & (16)\end{matrix}$

where the first term is the fidelity term and the second term is theregularization term. The fidelity term is modeled as the square errorbetween reconstructed signal and measurement H(x)=∥y−Φx∥₂ ². Theregularization term takes the form of a sparsity promoting norm.Depending on the property of the anatomy in question, different types ofsparsity promoting norms can be used in the optimization. When theconvex form of sparsity promoting norm is chosen, both algorithms A andB described below leverage convex optimization techniques.

Model A

The simplest form of sparsity can be modeled as an L1 normJ(x)=λ∥x∥_(l), derived from a Laplacian prior. With such regularization,a Model A algorithm can be obtained, which takes the form:

$\begin{matrix}{\hat{x} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {{y - {\Phi \; x}}}_{2}^{2}}} + {\lambda {x}_{1}}}} & (17)\end{matrix}$

Since there is no closed-form solution for Model A due to thenon-differentiability of the L1 norm, an iterative algorithm, such as,but not limited to, the linearized Bregman algorithm can be used tosolve the problem for this model.

Algorithm for Model A Linearized Bregman Algorithm:

Define Bregman divergence as D_(J) ^(P)(u,v)=J(u)−J(v)−

p,u−v

, where p denotes the subgradient of J.Seek a sequence of solution {u^(k)} in the iterative algorithm, whereeach subproblem is the solution of the following minimization:

$\begin{matrix}{x^{k + 1} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {D_{J}^{p^{k}}\left( {x,x^{k}} \right)}}} + {H(x)}}} & (18)\end{matrix}$

In order to solve for this problem more efficiently, a linearizationtechnique is applied to the fidelity term,

$\begin{matrix}{x^{k + 1} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {D_{J}^{p^{k}}\left( {x,x^{k}} \right)}}} + {\langle{{\nabla{H\left( x^{k} \right)}},x}\rangle} + {\frac{1}{2\alpha}{{x - x^{k}}}^{2}}}} & (19)\end{matrix}$

and an update for subgradient p is specified based on an optimalitycondition of the minimization to obtain the linearized Bregman algorithmshown below.

Linearized Bregman Algorithm Input: Φ, y, J(x), H(x), λ>0, α>0Algorithm:

Initialize: k=0, x⁰=0, p⁰=0while stopping criteria not satisfied do

$\begin{matrix}{x^{k + 1} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {D_{J}^{p^{k}}\left( {x,x^{k}} \right)}}} + {\langle{{\nabla{H\left( x^{k} \right)}},x}\rangle} + {\frac{1}{2\alpha}{{x - x^{k}}}^{2}}}} & (20) \\{p^{k + 1} = {p^{k} - {\nabla{H\left( x^{k} \right)}} - {\frac{1}{\alpha}\left( {x^{k + 1} - x^{k}} \right)}}} & (21)\end{matrix}$

If x^(k+1)=x^(k), then apply kicking

Update k=k+l  (22)

end whileOutput: optimal solution x^(k)In this algorithm, λ>0 is a regularization parameter that controls thedegree of sparsity, α>0 is a constant specified by the user, whichaffects the speed of the algorithm, k=1, . . . , n is an integer thatindexes the iterate of the algorithm, and p^(k) is the subgradient on J(x).

Model B

Depending on the structure of the anatomy in question, structuredsparsity can also be employed in the regularization term to improvereconstruction. One type of structured sparsity that can be used istotal variation (TV), which denotes a semi-norm with bounded variation:

TV(x)=∫|∇x|  (23)

Total variation characterizes an edge of the signal. In the case of bonestructures in the skull, since the anatomy is piecewise constant, totalvariation is suitable since sharp edges are preserved in thereconstruction. Therefore, by using J(x)=λ∫|∇x| and H(x)=∥y−Φx∥₂ ², thetotal variation regularized reconstruction leads to Model B:

$\begin{matrix}{\hat{x} = {{\arg \mspace{14mu} {\min\limits_{x}\mspace{14mu} {\lambda {\int{{\nabla x}}}}}} + {{y - {\Phi \; x}}}_{2}^{2}}} & (24)\end{matrix}$

Model B differs from Model A in that it imposes sparsity in the space ofedge variation, where (TV) serves as a global operator for preservingedges. Like Model A, there is no closed-form solution for Model B andtherefore, an iterative algorithm is used. In order to solve for such amodel, the split Bregman algorithm can be used.

Algorithm for Model B Split Bregman Algorithm:

The split Bregman algorithm splits the energy between the first term Jand second term H via variable splitting technique, by introducinganother variable u=g(x). The optimization therefore becomes:

$\begin{matrix}{{\arg \mspace{14mu} {\min\limits_{x,u}\mspace{14mu} {J(u)}}} + {H(x)} + {\frac{\mu}{2}{{u - {g(x)}}}_{2}^{2}}} & (25)\end{matrix}$

Solving for a sequence of minimizers is next, where each subproblemsolves for:

$\begin{matrix}{{\arg \mspace{14mu} {\min\limits_{x,u}\mspace{14mu} {D_{J}^{p}\left( {u,u^{k}} \right)}}} + {D_{H}^{p}\left( {x,x^{k}} \right)} + {\frac{\alpha}{2}{{u - {g(x)}}}_{2}^{2}}} & (26)\end{matrix}$

The terms J and H are replaced by the associated Bregman divergencerespectively.

Split Bregman Algorithm Input: Φ, y, J(x), H(x), λ>0, α>0 Algorithm:

Initialize: k=0, x⁰=0, u⁰=0, p⁰=0, p_(u) ⁰=0while stopping criteria not satisfied do

$\begin{matrix}{\left( {x^{k + 1},u^{k + 1}} \right) = {{\arg \mspace{14mu} {\min\limits_{x,u}\mspace{14mu} {J(u)}}} + {H(x)} - {\langle{p_{x}^{k},{x - x^{k}}}\rangle} - {\langle{p_{u}^{k},{u - u^{k}}}\rangle} + {\frac{\alpha}{2}{{u - {g(x)}}}_{2}^{2}}}} & (27) \\{\mspace{76mu} {p_{x}^{k + 1} = {p_{x}^{k} - {\mu {\nabla{g\left( x^{k + 1} \right)}^{T}}\left( {{g\left( x^{k + 1} \right)} - u^{k + 1}} \right)}}}} & (28) \\{\mspace{76mu} {p_{u}^{k + 1} = {p_{u}^{k} - {\alpha \left( {u^{k + 1} - {g\left( x^{k + 1} \right)}} \right)}}}} & (29) \\{\mspace{76mu} {{{Update}\mspace{14mu} k} = {k + 1}}} & (30)\end{matrix}$

end whileOutput: optimal solution x^(k)In this algorithm, λ>0 is a regularization parameter that controls thedegree of sparsity, α>0 is a constant specified by the user, whichaffects the speed of the algorithm, k=1, . . . , n is an integer thatindexes the iterate of the algorithm, p_(x) ^(k) is the subgradient onH(x), and p_(u) ^(k) is the subgradient on J(u).

As a regression problem, the sparse nature of projections can beexploited, and L1 regularization as part of the optimization objectivecan be used. Computationally efficient and accurate algorithms as shownabove via convex optimization can also be used. Structured sparsity suchas Total Variation (TV) can be employed as a regularization form duringthe reconstruction, if the target profile is piece-wise constant. ModelA or Model B can be used to perform the 3D volume reconstruction. Theinput of the algorithm can include: 1) design matrix Φ, which is thecollection of projections, and 2) measurement y, which is the detectedtherapeutic energy. The output of the algorithm can include the 3Dvolume of therapeutic energy distribution.

In contrast to filtered back projection algorithms used in traditionalCT reconstruction, compressive sensing algorithms are associated with aniterative method and need fewer projections to reconstruct the volume.As shown in FIGS. 8A-8C, even with fewer number of projection angles,compressive sensing leads to a better image reconstruction than thefiltered back projection. Sparsity promoting norms are key to thecompressive sensing algorithm, because they exploit prior information inthe observations to improve reconstruction. The algorithm is iterativein nature. In each iteration, the system 100 evaluates the residualreconstruction error, and uses the algorithm to update the currentiteration. Convex relaxation of sparsity promoting norms can also beused as regularizations, and therefore convex optimization techniquescan be used to solve for Models A and B. When the regularization takesthe form of L1 norm, the linearized Bregman algorithm can be used tosolve for it. When the regularization takes the form of Total Variationsemi-norm, the split Bregman algorithm can be used.

If the reconstruction is very good compared to the normal environment,the threshold for generating an alarm signal when a displacement ispresent can have a first value. If the reconstruction is not very good,for example, when the number of samples is very low or when theprojection angles are too coherent, a different, second threshold valuecan be set for generating an alarm signal in case of a displacement.

Theoretical Guarantees

The quality of the 3D volume reconstruction depends on the number ofprojections and geometry of the radiation therapy device. Theoreticalguarantees can also be derived, and statistical simulations can beperformed to determine the minimum number of projections required toachieve volume reconstruction of sufficient quality. The minimum numberof projections will depend on how sparse the anatomy in question is andhow many unknown variables are present. For a linear system, such as inModel A, if the measurement is randomly sampled, the anatomy isk-sparse, the dimension of unknown variables is n, then the minimumnumber of samples can be:

m≥Ck log(n/k)  (31)

where C is a constant. A variant of such guarantee can be explored withrespect to the specific detector 4 design, since the geometry of theproblem poses constraints on the randomness of measurement, and thusoffers different bounds. Due to the local operator used in thereconstruction algorithm, the nature of the computation can be parallel.Thus, parallel computing architectures such as GPU can be used toaccelerate the computations.

Dosimetry

In addition to the real-time correlation and 3D actual target volumereconstruction steps, the radiation dose delivered to the target canalso be computed in real-time and added as a third real-time targetvalidation parameter (metric).

During the treatment, the radiation detector 4 in the imaging device 30collects radiation projection data. The radiation dose calculationmodule (shown in FIG. 12) includes computational algorithms configuredto extract both the shape and dose distribution from the collectedin-treatment images and to compare this information with the library ofpredicted radiation doses stored in the computer 50. In intensitymodulated treatment therapies, additional information regarding thevariation in the shape of the therapeutic beam based on the differentprojection angles, and even different time points, can also be used. Thecombination of the shape and dose distribution information helps todetermine whether any inconsistency in the actual treatment dosedelivered is due to incorrect dose delivery or the target displacement.The radiation calculation module calculates the total dose deliveredover the entire surgery as a metric. During the treatment, the system100 can continuously compare the actual delivered radiation dose withthe prescribed dose. If the delivered dose is significantly differentfrom the prescribed dose, under certain statistical criterion, afeedback signal can be sent to the controller 40 to interrupt theradiosurgery or radiation treatment therapy. A dose evaluation method asdescribed in U.S. application Ser. No. 13/650,980, filed Oct. 12, 2012,titled “SYSTEMS, DEVICES, AND METHODS FOR QUALITY ASSURANCE OF RADIATIONTHERAPY”, the content of which is incorporated herein by reference inits entirety, can be used to quantitatively evaluate the differencesbetween the measured and the predicted dose distributions.

Closed-Loop Feedback

If a displacement of the target is detected, the analysis module sends aclosed-loop feedback signal to the linear accelerator controller 40 tostop radiation treatment and/or to allow the surgeon to adjust thedelivery of the radiation therapy treatment during the therapy session.The system 100 can perform this function based on instructions providedin the computation module to determine the displacement. This can alsobe done by combining the displacement information obtained from thecorrelation and the actual 3D target volume reconstruction steps withthe dosimetry information in the dosimeter monitoring step. If it isdetermined by the analysis module that the displacement is outside apredetermined threshold range, a feedback module generates a feedbacksignal which is sent to the controller 40, thereby allowing the surgeonto stop the surgery during the treatment and recalibrate the radiationtherapy/radiosurgery device 10. Additional information obtained from a(keV) X-ray imaging device (not shown), which can be turned on duringthe treatment, can augment this process and improve displacementdetection.

Recalibration

In addition to the stop signal, which is a binary decision signal, anadditional parameter (metric) can be computed to guide the recalibrationprocess. A repositioning signal is helpful for the surgeon to determinewhere and how to place the patient 5 with respect to the isocenter ofradiation inside the radiation radiosurgery device 10, after stoppingfor recalibration. Given the library of DRRs computed and stored priorto treatment, and the collected live therapeutic in-treatment imagesacquired for displacement detection, a gradient field of signal changecan be computed. A search direction can be optimized to seek thedirection of the displacement in order to maximize the correlationbetween the in-treatment projection images and the DRRs. Since thedisplacement of the patient 5 is relatively small for radiosurgeries, itcan be assumed that the perturbations are linear and that a uniquemaximum exists for the correlation landscape. In doing so, theadjustment and the direction of the adjustment can be determined duringthe recalibration process.

For example, given a span of isocenters, a library of DRR images foreach angle can be simulated. This is denoted by L(i,j,k), where (i,j,k)denotes the coordinate for isocenters, i=1 . . . n, j=1 . . . n, k=1 . .. n. In other words, a collection of DRR images based on a 3D grid ofall possible isocenters is obtained. During the treatment, an image isacquired using the therapeutic imaging device 30 for that same angle,denoted by I(i_(c),j_(c),k_(c)). The correlation between theI(x_(o),y_(o),z_(o)) and each image in the DRR library can be computedusing:

C(i,j,k)=corr(L(i,j,k),I(i _(c) ,j _(c) ,k _(c)))  (32)

A gradient field can be computed based on a correlation map:

$\begin{matrix}{g = {\left( {g_{x},g_{y},g_{z}} \right) = \left( {\frac{\partial C}{\partial x},\frac{\partial C}{\partial y},\frac{\partial C}{\partial z}} \right)}} & (33)\end{matrix}$

Once a displacement signal is detected, the direction to mitigate thedisplacement can be determined by going in the opposite direction as thegradient field. To minimize the correlation, an estimate of the stepsize to be taken can be computed using:

$\begin{matrix}{\left( {{dx},{dy},{dz}} \right) = {{\arg \mspace{14mu} {\min\limits_{{dx},{dy},{dz}}\mspace{14mu} {C\left( {i,j,k} \right)}}} - {\langle{\left( {g_{x},g_{y},g_{z}} \right),\left( {{dx},{dy},{dz}} \right)}\rangle}}} & (34)\end{matrix}$

When a step size and direction are computed, they can be sent asfeedback to the controller 40 for the linear accelerator 2 to close theloop. This information act as a fourth metric and can assist the surgeonto recalibrate the device 10 and to adjust the aim of the therapeuticbeam.

The real-time target validation process steps are shown in FIGS. 9-11.During radiation treatment, in-treatment images are generated in S42 bythe digital imaging device 30. Also, in S41 the radiation detector 4detects the radiation dose delivered to the target. The livein-treatment images and the dosimeter information are sent in S51 to thecomputer 50 for processing. Using compressive sensing techniques, asdiscussed hereinabove, for example, in real-time, the computer 50, inparallel, computes similarity measurements between the live images andthe previously generated DRRs in S52, reconstructs the 3D targetedvolume in S53, and calculates the delivered radiation dose in S54. Thesystem then computes a displacement measurement signal in S55, and upondetermination that the displacement measurement signal is larger than athreshold signal, transmits a feedback signal in S61 to the controller40 to either automatically stop the radiation treatment or to allow amedical personnel to stop the treatment.

The similarity measurement computation, according to variousembodiments, is shown in FIG. 10. In Step S521, prior to radiationtreatment, the system 100 predicts the approximate trajectory and X-rayfluence of the treatment beams by synthesizing DRRs. Upon receiving thein-treatment live images, the system 100 generates actual trajectory andfluence of treatment beams from the live in-treatment images in S522 andcompares in S523 and correlates in S524 in real-time the beamtrajectories and locations of targeted energies of the two sets ofimages. Using compressive sensing techniques as discussed herein, thesystem 100 detects in S525 whether there are significant changes in thecorrelation and computes a target displacement in S526.

The 3D actual target volume reconstruction is shown in FIG. 11.

Using parallel computing techniques, while the similarity measures arecomputed, as shown in FIG. 10, the system 100 can also use compressivesensing techniques as discussed herein to formulate the geometry of thetherapeutic beams as a design matrix in S531, the transmitted X-rayfluences as observations in S532, and the sought target positions asexplanatory variables in S533. Using compressive sensing techniques asdiscussed previously, in S534 together with the application of totalvariation as regularization in S535 and optimization techniques in S536,the system 100 in S537 reconstructs the actual 3D target volume. Ifthere is a difference between the actual 3D target volume and theplanned 3D target volume, a displacement is registered and a feedbacksignal is generated, as shown in FIG. 9.

A non-transitory computer readable medium can be used to store thesoftware or programmed instructions and data which when executed by acomputer processing system 50 causes the system to perform variousmethods of the present invention, as discussed herein. The executablesoftware and data may be stored in various places, including, forexample, the memory and storage of the computer processing system 50 orany other device that is capable of storing software and/or data.

Accordingly, embodiments of real-time target validation systems, methodsand computer program products have been disclosed. Many alternatives,modifications, and variations are enabled by the present disclosure.Features of the disclosed embodiments can be combined, rearranged,omitted, etc. within the scope of the invention to produce additionalembodiments.

Further, a method of adjusting further administration of a therapeuticradiation beam to a target during a radiation treatment session is alsodisclosed, the adjusting being performed based on a determination madeduring the treatment session that there is a change in a parameter ofthe target, the determination being performed based on target projectionimages continually generated during the treatment using the therapeuticradiation beam.

Also, a non-transitory computer-readable storage medium is disclosedupon which is embodied a sequence of programmed instructions forvalidating in real-time a target exposed to radiation therapy in aradiation therapy treatment system including a computer processingsystem which executes the sequence of programmed instructions embodiedon the computer-readable storage medium to cause the computer processingsystem to perform the steps of: comparing X-ray images of a targetvolume obtained during treatment using a therapeutic radiation beam witha corresponding previously obtained 2D projections of the target volume;determining similarity measures between the X-ray images and thereference images; detecting a displacement of the target volume based onthe similarity measures; comparing the displacement with a thresholddisplacement; and generating a feedback signal if the displacementexceeds the threshold displacement.

Further, systems and methods of adjusting further administration of atherapeutic radiation beam to a target during a radiation treatmentsession based on a determination made during the treatment session of achange in a parameter of the target, the determination being performedbased on target projection images continually generated during thetreatment using the therapeutic radiation beam, are also disclosed.

Furthermore, certain features of the disclosed embodiments may sometimesbe used to advantage without a corresponding use of other features.Accordingly, Applicants intend to embrace all such alternatives,modifications, equivalents, and variations that are within the spiritand scope of the present disclosure.

While embodiments and applications of this invention have been shown anddescribed, it would be apparent to those skilled in the art that manymore modifications are possible without departing from the inventiveconcepts herein. The invention is not limited to the description of theembodiments contained herein, but rather is defined by the claimsappended hereto and their equivalents.

1-51. (canceled)
 52. A method for real-time validation of a radiationtreatment session, comprising: obtaining images of a target undertreatment; comparing, over a series of projection angles, the obtainedimages with corresponding reference images; and evaluating whether thereis a change in a parameter of the treatment session by: determining afirst difference between the compared images at a first projectionangle; determining a second difference between the compared images at asecond, subsequent projection angle; computing the difference betweenthe first and second differences; and comparing the computed differencewith a predetermine threshold, wherein a computed difference above thepredetermined threshold indicates a change in one or more treatmentparameters.
 53. The method of claim 52, wherein the treatment parametersinclude target spatial position, target orientation, target volumeposition, beam trajectory through the target, and radiation dose. 54.The method of claim 53, wherein a change in the treatment parameterindicates one or more of target displacement and radiation dose deliveryerror.
 55. The method of claim 52, wherein the evaluating includesevaluating a similarity between a beam trajectory in a 3D target volumereconstructed from the obtained images and a 3D target volumereconstructed from the reference images.
 56. The method of claim 55,wherein the 3D reconstructing includes using a compressive sensingcomputation algorithm.
 57. The method of claim 56, further comprisingvalidating spatial position and orientation of the target volume basedon the reconstructed 3D target volumes.
 58. The method of claim 57,wherein the reconstructing is in real-time.
 59. The method of claim 52,wherein the evaluating includes evaluating, in real-time, a similaritybetween the total radiation dose in a target volume determined from theobtained images and the total radiation dose in the target volumedetermined from the reference images.
 60. The method of claim 52,wherein the comparing and evaluating are continually performed duringthe treatment session.
 61. The method of claim 52, further comprisinggenerating a feedback signal upon indication of a change in one or moreparameters.
 62. The method of claim 61, further comprising applying anadjustment in the treatment session in response to the feedback signal.63. A system for real-time validation of a radiation treatment session,comprising: an imaging device to generate a series of images of a targetvolume exposed to radiation; and a computer processing system configuredto: obtain the series of images; compare, over a series of projectionangles, the obtained images with corresponding reference images; andevaluate whether there is a change in a parameter of the treatmentsession by: determining a first difference between the compared imagesat a first projection angle; determining a second difference between thecompared images at a second, subsequent projection angle; computing thedifference between the first and second differences; and comparing thecomputed difference with a predetermine threshold, wherein a computeddifference above the predetermined threshold indicates a change in oneor more treatment parameters.
 64. The system of claim 63, wherein thetreatment parameters include target spatial position, targetorientation, target volume position, beam trajectory through the target,and radiation dose, and wherein a change in the treatment parameterindicates one or more of target displacement and radiation dose deliveryerror.
 65. The system of claim 63, wherein the evaluating includesevaluating a similarity between a beam trajectory in a 3D target volumereconstructed from the obtained images and a 3D target volumereconstructed from the reference images, the 3D reconstructing includingusing a compressive sensing computation algorithm, wherein thereconstructing is in real-time, and wherein the system further comprisesvalidating spatial position and orientation of the target volume basedon the reconstructed 3D target volumes.
 66. The system of claim 63,wherein the evaluating includes evaluating, in real-time, a similaritybetween the total radiation dose in a target volume determined from theobtained images and the total radiation dose in the target volumedetermined from the reference images.
 67. The system of claim 63,wherein the computer processing system is further configured to generatea feedback signal upon indication of a change in one or more parameters,and apply an adjustment in the treatment session in response to thefeedback signal.
 68. A non-transitory computer-readable storage mediumupon which is embodied a sequence of programmed instructions which whenexecuted by a computer processing system causes the computer processingsystem to: obtain a series of images of a target volume; compare, over aseries of projection angles, the obtained images with correspondingreference images; and evaluate whether there is a change in a parameterof the treatment session by: determining a first difference between thecompared images at a first projection angle; determining a seconddifference between the compared images at a second, subsequentprojection angle; computing the difference between the first and seconddifferences; and comparing the computed difference with a predeterminethreshold, wherein a computed difference above the predeterminedthreshold indicates a change in one or more treatment parameters. 69.The computer-readable medium of claim 68, further causing the computerprocessing system to: evaluate a similarity between a beam trajectory ina 3D target volume reconstructed from the obtained images and a 3Dtarget volume reconstructed from the reference images, the 3Dreconstructing including using a compressive sensing computationalgorithm, wherein the reconstructing is in real-time; and validatespatial position and orientation of the target volume based on thereconstructed 3D target volumes.
 70. The computer-readable medium ofclaim 68, further causing the computer processing system to evaluate, inreal-time, a similarity between the total radiation dose in a targetvolume determined from the obtained images and the total radiation dosein the target volume determined from the reference images.
 71. Thecomputer-readable medium of claim 68, further causing the computerprocessing system to generate a feedback signal upon indication of achange in one or more parameters, and apply an adjustment in thetreatment session in response to the feedback signal.